Instructions to use abigailp/m3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abigailp/m3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="abigailp/m3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("abigailp/m3") model = AutoModelForSequenceClassification.from_pretrained("abigailp/m3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 3
Browse files
pytorch_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 267855533
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:e3653d1af818da9013e0dc428b7381719655854fa77e0226a858f572abe1c988
|
| 3 |
size 267855533
|
runs/Jan31_13-45-40_b152d93c994a/events.out.tfevents.1675172810.b152d93c994a.371.0
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
-
size
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:032068dd64752d75db2ba1cca2b8a9430904419c02040937aad2dae1a00995f1
|
| 3 |
+
size 4743
|